ATLAS v2.0 Dataset

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=1271), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes training (public. n=655), test (masks hidden, n=300), and generalizability (completely hidden, n=316) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test and generalizability datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke rehabilitation research.

Official Paper: https://www.nature.com/articles/s41597-022-01401-7

精确的病灶分割在卒中康复研究领域对于病灶负荷的量化及图像处理的准确性至关重要。目前应用于康复研究的T1加权（T1w）MRI的自动病灶分割方法，虽普遍使用，但准确性和可靠性仍显不足。手动分割虽为金标准，但耗时冗长，主观性强，且需要深厚的神经解剖学专业知识。然而，众多基于ATLAS v1.2开发的分割方法报告准确性偏低，且未公开获取或验证不充分，限制了其在领域的应用。在本研究中，我们推出了ATLAS v2.0（N=1271），这是一个包含T1w卒中MRI和手动分割病灶掩膜的更大数据集，其中包含训练集（公开，n=655）、测试集（掩膜隐藏，n=300）和泛化集（完全隐藏，n=316）数据。利用这个更大的样本集进行算法开发，有望引领至更稳健的解决方案，而隐藏的测试集和泛化集则允许通过分割挑战进行无偏的绩效评估。我们预期ATLAS v2.0将推动算法的改进，从而促进大规模卒中康复研究。